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Don Pierson, Uppsala University, Sweden Eleanor Jennings Dundalk Inst of Tech Ireland

PROGNOS Predicting in-Lake Responses to Change Using Near-real-time Models. Don Pierson, Uppsala University, Sweden Eleanor Jennings Dundalk Inst of Tech Ireland Elvira de Eyto, Marine Insitute, Ireland Erik Jeppesen & Dennis Trolle Aarhus University, Denmark

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Don Pierson, Uppsala University, Sweden Eleanor Jennings Dundalk Inst of Tech Ireland

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  1. PROGNOSPredicting in-Lake Responses to Change Using Near-real-time Models Don Pierson, Uppsala University, Sweden Eleanor JenningsDundalk Inst of TechIreland Elvira de Eyto,Marine Insitute,Ireland Erik Jeppesen & Dennis Trolle Aarhus University, Denmark Karsten Bolding and Jorn Bruggeman Bolding & Bruggeman ApS, Denmark Raoul-Marie Couture, Isabel Seifert-Dähnn & Jose-Luis.Guerrero Norwegian Inst for Water Research Gideon GalIsrael Oceanographic and Limnological Research,Israel

  2. Objectives • Demonstrate the value of High Frequecy (HF) water quality monitoring to provide information to support water managment decisions • Information on the present state of the lake • Couple HF monitoring data to water quality models in order to provide short-term water quality forecasts. • Information on the future state of the lake • Enhanced information to support water management decisions • Inceased value of HF water quality monitoring data • Issues Considered • Algal Blooms • DOC concentrations

  3. Scientific and Technological Progress • Ongoing collection of high frequency monitoring data at all case study sites that can be used to test, calibrate and verify the water quality models. • Identified specific events in the above data that can serve as relevant test cases for the modeling system. • Developing a common modular modeling system that can be used to make predictions of algal blooms and DOC concentrations. • Calibrated and verified the GOTM hydrothermal model at all sites used by the PROGNOS consortium. • Ongoing testing of water quality modules which predict phytoplankton and DOC dynamics. Calibrate and verify these as model modules. • Development of modeling workflows that will allow automated monitoring data to be incorporated into model simulations to better set initial conditions and parameter values prior to a forecast simulation.

  4. Calibration and Verification of Hydrothermal Model Identification of Events DOC - Ireland Lake Erken - Sweden Measured Temperature Calibrated Temperature Phytoplankton Blooms - Sweden

  5. Prediction of DOC – CDOM Fluorescence INCA C Watershed Model - Ireland HF DOC DOMCast – PROGNOS Lake DOC model. Developed at NIVA Norway

  6. Prediction of Phytoplankton – Chlorophyll Concentration Experimental Mesocosíms - Denmark Lake Ravn - Denmark

  7. Collaboration, Coordination and Mobility • Project Meetings (coordinated with modeling workshop) • Kickoff Uppsala, Sweden (June 2016) • Year 1 NIVA Oslo Norway (June 2017) • Year 2 Silkeborg Denmark (June 2018) • Modelling Workshops • Year 1 – November 2016 (Denmark) • Year 2 – October 2017 (Denmark) • SKYPE teleconferences • On average 8 per year. Facilitate project planning and discussion of modeling and other issues • Ad Hoc Meetings • GLEON 18 July 2016 • MANTEL ITN network Ireland January 2017 • GLEON 19 November 2017 • Slack • Provides ongoing dialog on modeling issues

  8. Stakeholder/Industry Engagement • Modest stakeholder participation in annual meetings • Meetings with individual stakeholders • Stockholm Vatten • Irish EPA • Dedicated sections of web page for stakeholders • Water manager outreach • Cost benefit analysis • Will disseminate results at World Water Week 2019 • Dedicated stakeholder workshop 2019

  9. Dissemination of the results • PROGNOS Web Page (http://prognoswater.org/) • Over 21,000 unique visitors up to Jan 2018 • WP descriptions • Site Information • Popular Blog site • Facebook(https://www.facebook.com/PROGNOSproject/) • Twitter • 11,900 impressions in the period from June 2016 to December 2017 and 71 tweets. Mainly related to web site blog • 13 presentations on the project to date, 6 of which were targeted towards stakeholders • 4 publications (mainly mesocosim work). Several additional manuscripts under way

  10. Identified problems or specific risks • Stakeholder involvement • Pressed for time – high level of involvement in project meetings and planning not realistic. • Largely solved by other means of outreach especially site visits • Water Quality modeling • Somewhat behind schedule. Time consuming work. • Water quality calibration. • Development of DOC models. • Inclusion of watershed inputs. • However once completed these tasks can be incorporated in to modeling workflows.

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